考虑差异化服务时间的多车型电动汽车路径优化与充电策略研究

在政府政策大力支持以及社会环境意识不断增长的背景下，电动汽车在物流配送行业快速普及。电动汽车参与的物流配送服务需要物流专员、电动汽车和顾客三方协作完成。因此，在传统车辆配送路径优化的基础上，车辆的多样性、充电策略、人车的匹配以及服务时间差异化等因素都会影响物流运营成本。本文提出了考虑差异化服务成本的多车型电动汽车路径优化与充电策略问题并建立了该问题的整数规划数学模型。其次，提出了混合启发式算法MCWGATS，并通过多组算例验证了算法的有效性。最后，采用多组算例分析了多车型和差异化服务时间对运营成本的影响。实验结果表明，该模型有助于物流企业提高人员、物流车辆、服务时间等资源的利用效率，降低运营成本。

Study on Heterogeneous Electric Vehicle Routing and Batterycharging Problem with the Consideration of Differentiated Service Cost

With the substantial support from government and development of environmental awareness, electric vehicles grow more and more popular in logistics fields. Small package shipping is in the back end of logistics distribution and performs deliveries from local deports to customers. Logistic operational cost may be affected by many factors, including heterogeneous vehicles, staff assignment and differentiated service time. An effective utilization of logistics deliverer can bring economic benefits, which has been paid more attention by logistics enterprise gradually. Logistics distribution service needs the cooperation of the professional delivery staffs, electric vehicles and numerous customers, with a great deal of interplay between staff assignment, vehicle capacity and customer distribution. A heterogeneous electric vehicles routing and battery charging problem is presented with the consideration of differentiated service time. The problem is formulated as an integer programming model. Then, a heuristic MCWGATS and is proposed to solve the problem. MCWGATS is composed of four parts:modified Clark and Wright saving algorithm, hybrid genetic algorithm, local search and tabu search. The modified savings mechanism is used to optimize the service routes in the path subproblem. And then hybrid genetic algorithm is proposed to get assignment strategies. Finally, current strategy is optimized by LS and TS. Compared with the MIP solver of CPLEX on small-size instances, MCWGATS can solve the problem within a shorter computing time and get reasonable solutions. Then, parameter analysis of heterogeneous vehicles and differentiated service time is systematically conducted for this problem. The results show that the model contributes to help logistics firms to improve the utilization efficiency of such resources as staff, electric vehicles, service time, etc.